AI enrichment is using AI models to add intelligence to contact and account data — summarizing a company's recent news, inferring job responsibilities from a title, identifying buying signals from LinkedIn activity, or generating personalization angles from public information. Beyond basic data enrichment (adding email addresses, phone numbers), AI enrichment creates the context that makes outreach actually personalized.
For example, AI enrichment on a prospect record might generate: 'Sarah recently posted about scaling her SDR team from 5 to 15 — likely building a new outbound program. Personalization angle: acknowledge the scale challenge and connect to your platform's SDR onboarding features.'
AI enrichment is a core part of our outbound stack — it's what separates 'Hi [First Name]' personalization from research that makes prospects reply.
Related Terms
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Book a free strategy call →Agentic AI
Agentic AI refers to AI systems that can plan, take actions, use tools, and complete multi-step tasks autonomously — going beyond generating text to actually doing work.
AI Agent
An AI agent is an LLM-powered system that can autonomously use tools, access data, and complete tasks — as opposed to a simple chatbot that only responds to single prompts.
Autonomous Workflow
An autonomous workflow is a multi-step automated process that runs without human intervention — trigger, conditions, actions, branches, and loops all executing on schedule or in response to events.
Human-in-the-Loop (HITL)
Human-in-the-loop describes AI automation workflows that include a human review or approval step before consequential actions are taken — particularly sending outreach, making calls, or publishing content.
Large Language Model (LLM)
An LLM is the AI model underlying most modern AI tools — GPT-4, Claude, Gemini, Llama.
Prompt Engineering
Prompt engineering is the practice of designing inputs to AI models to get better, more consistent outputs.